CN110451395B - Escalator fault monitoring method - Google Patents

Escalator fault monitoring method Download PDF

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Publication number
CN110451395B
CN110451395B CN201910653549.3A CN201910653549A CN110451395B CN 110451395 B CN110451395 B CN 110451395B CN 201910653549 A CN201910653549 A CN 201910653549A CN 110451395 B CN110451395 B CN 110451395B
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escalator
characteristic
data
bearing
signals
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CN110451395A (en
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倪伟
梁衡
吴健申
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Guangdong Global Smart Technology Co ltd
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Guangdong Global Smart Technology Co ltd
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B66HOISTING; LIFTING; HAULING
    • B66BELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B29/00Safety devices of escalators or moving walkways
    • B66B29/005Applications of security monitors

Abstract

The invention discloses an escalator fault monitoring method, which comprises a data acquisition step, wherein working conditions are distinguished according to the current and the rotating speed of an escalator motor during the operation of an escalator, and vibration signals of a main driving bearing and a tensioning frame bearing are acquired according to the working conditions; a data judgment step, namely judging the validity of the signal by utilizing a quotient function; a data elimination step, namely eliminating interference components in the signals by using a self-adaptive threshold value to obtain effective signals; a data characteristic index calculating step, which calculates the root mean square RMS, the variance Var and the peak value Xpp of the characteristic index by using effective signal data; and a state evaluation step, namely establishing a characteristic vector X formed by characteristic indexes, substituting the characteristic vector X into a logarithmic linear regression model, and solving the probability that the main drive bearing and the tensioning frame bearing are normal. The escalator fault monitoring method can monitor the escalator in real time, evaluate the state of the escalator, perform early warning and maintenance adjustment in time according to the state of the escalator, and avoid the escalator from being in fault.

Description

Escalator fault monitoring method
Technical Field
The invention relates to the field of elevators, in particular to a method for monitoring escalator faults.
Background
In the prior art, the escalator is widely applied to public places such as markets, airports, stations, pedestrian overpasses, rail transit stations and the like, and brings great convenience to life of people.
Meanwhile, due to the use abrasion, the escalator can be in failure, and safety accidents caused by the failure of the escalator are frequent.
The investigation shows that the failure of the escalator is mainly concentrated on the main driving bearing and the tensioning frame bearing of the escalator.
Conventionally, maintenance is performed by periodically injecting lubricating oil.
However, the existing mode cannot monitor the escalator in real time, cannot accurately predict and judge the state of the escalator, and cannot prevent the problem of escalator faults.
Disclosure of Invention
The invention aims to solve the technical problems and provides an escalator fault monitoring method which can monitor an escalator in real time, evaluate the state of the escalator, perform early warning and maintenance adjustment in time according to the state of the escalator and avoid the escalator from being in fault.
The invention is realized by the following technical scheme:
the invention provides a method for monitoring the fault of an escalator, which comprises the following steps:
and a data acquisition step, namely distinguishing working conditions according to the current and the rotating speed of the escalator motor when the escalator runs, and acquiring vibration signals of the main driving bearing and the tensioning frame bearing according to the working conditions.
A data judgment step, judging the validity of the signal by utilizing a quotient function, wherein the quotient function is SIG _ CVG ═max (difference of SIG _ Xpp) represents the difference between the maximum peak value and the maximum peak value at different time instants, SIG _ Xpp represents the peak value of the signal in the period of time, when the quotient function approaches 0, the signal is free of interference, otherwise, the signal is interfered.
And a data elimination step, namely eliminating interference components in the signals by using a self-adaptive Threshold value to obtain effective signals, wherein the Threshold value formula is Threshold (mu + k sigma), mu is the mean value of the signals, sigma is the standard deviation of the signals, and k is a selected value.
And a data characteristic index calculating step, namely calculating the root mean square RMS, the variance Var and the peak value Xpp of the characteristic index by using the effective signal data.
And a state evaluation step, namely establishing a characteristic vector X formed by characteristic indexes, substituting the characteristic vector X into a logarithmic linear regression model, and solving the probability that the main drive bearing and the tensioning frame bearing belong to normal, wherein the logarithmic linear regression model has the formula ofω represents the coefficient of the regression model and b represents the intercept of the regression model.
The beneficial effects are that: compared with the prior art, the escalator fault monitoring method firstly obtains vibration data by monitoring the vibration of the main driving bearing and the tensioning frame bearing on the escalator; then, identifying interference signals through a quotient function; further, noise elimination and interference signal removal are carried out through a self-adaptive threshold value, and effective vibration signals belonging to a main driving shaft and a tensioning frame bearing are obtained; then obtaining a characteristic vector by solving a characteristic index of the vibration signal; finally, substituting the characteristic vector into a regression model to obtain the probability that the main driving bearing and the tensioning frame bearing belong to normal states; therefore, the running state of the escalator can be evaluated, the fault of the bearing is judged in advance, maintenance is carried out in advance, the fault is avoided, and safety accidents are avoided.
In the escalator fault monitoring method according to the first aspect of the present invention, it is preferable that the data determination step use SIG _ CVG of 0.25 as a determination reference.
Advantageously, in order to reasonably identify the interference signal and avoid the vibration signal from being mistakenly rejected, the quotient function value should be properly selected, and it is most appropriate to set SIG _ CVG to 0.25.
In the escalator fault monitoring method according to the first aspect of the present invention, preferably, in the data eliminating step, k is selected to be 3.
Advantageously, k-3 meets the test requirements, and abnormal interference signals can be better removed.
According to the escalator fault monitoring method in the first aspect of the present invention, preferably, in the data characteristic index obtaining step, the characteristic indexes to be obtained further include a peak value Xp, a skewness skew, a peak value index Cf, and a kurtosis.
The method has the advantages that omission is avoided by solving a plurality of characteristic indexes, and incomplete bearing state evaluation is avoided.
According to the escalator fault monitoring method in the first aspect of the invention, further, in the data characteristic index obtaining step, a characteristic matrix of the characteristic index and states of the main drive bearing and the tension frame bearing is also required to be established, and the correlation between the characteristic index and the states of the main drive bearing and the tension frame bearing is evaluated.
The method has the advantages that irrelevant or poor relevant characteristic indexes are eliminated, and misjudgment on the bearing state is avoided.
According to the escalator fault monitoring method in the first aspect of the invention, further, in the state evaluation step, a feature vector formed by feature indexes is established according to the correlation between the feature indexes and the states of the main drive bearing and the tension frame bearing.
The method has the advantages that the judgment function precisely related to the characteristic indexes is obtained by establishing the characteristic vectors, and the bearing state is accurately judged.
In the escalator fault monitoring method according to the first aspect of the present invention, preferably, in the state estimating step, ω and b are found by a maximum value of the likelihood function L (ω) using a maximum likelihood method,
the method has the advantages that the maximum likelihood method is used for obtaining the model parameter values, and the risk of evaluation errors caused by the value of the model parameters is avoided.
In the escalator fault monitoring method according to the first aspect of the present invention, further, in the state evaluating step, the maximum value of the likelihood function L (ω), max [ L (ω), is found by using a random gradient method]=max(∑[yi*logf(xi)+(1-yi)*log(1-f(xi))])。
The maximum value of the likelihood function L (omega) is obtained by using a random gradient method, so that the obtaining difficulty is reduced.
Drawings
In order to more clearly illustrate the technical solution in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below.
It is clear that the described figures are only some embodiments of the invention, not all embodiments, and that a person skilled in the art can also derive other designs and figures from them without inventive effort.
Fig. 1 is a flow chart of an escalator fault monitoring method of the present invention;
FIG. 2 is a logic diagram of the monitoring method of FIG. 1.
Detailed Description
Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
As shown in fig. 1, an escalator fault monitoring method is provided, which comprises the following 5 steps.
1. And a data acquisition step, namely distinguishing working conditions according to the current and the rotating speed of the escalator motor when the escalator runs, and acquiring vibration signals of the main driving bearing and the tensioning frame bearing according to the working conditions.
2. A data judgment step, judging the validity of the signal by utilizing a quotient function, wherein the quotient function is max (difference of SIG _ Xpp) represents the maximum peak-to-peak value at different timesIn contrast, SIG _ Xpp represents the peak-to-peak value of the signal during this time, when the quotient approaches 0, it means that the signal is not interfering, and vice versa, it means that the signal is interfered.
3. And a data elimination step, namely eliminating interference components in the signals by using a self-adaptive Threshold value to obtain effective signals, wherein the Threshold value formula is Threshold (mu + k sigma), mu is the mean value of the signals, sigma is the standard deviation of the signals, and k is a selected value.
4. And a data characteristic index calculating step, namely calculating the root mean square RMS, the variance Var and the peak value Xpp of the characteristic index by using the effective signal data.
5. And a state evaluation step, namely establishing a characteristic vector X formed by characteristic indexes, substituting the characteristic vector X into a logarithmic linear regression model, and solving the probability that the main drive bearing and the tensioning frame bearing belong to normal, wherein the logarithmic linear regression model has the formula ofω represents the coefficient of the regression model and b represents the intercept of the regression model.
In brief, the operation principle of the monitoring method of this embodiment is as described in fig. 2, and is detailed as follows:
vibration data is obtained by monitoring the vibration of a main driving bearing and a tensioning frame bearing on the escalator.
And identifying the interference signal through a quotient function.
Furthermore, the interference signals are eliminated through self-adaptive threshold value noise elimination, and effective vibration signals belonging to the main driving shaft and the tensioning frame bearing are obtained.
And obtaining a characteristic vector by solving the characteristic index of the vibration signal.
And finally, substituting the characteristic vector into a regression model to obtain the probability that the main driving bearing and the tensioning frame bearing belong to a normal state.
After the probability that the bearing belongs to the normal state is obtained, the probability that the bearing belongs to the fault state is also obtained, so that the running state of the escalator can be evaluated, the fault of the bearing is judged in advance, the maintenance is carried out in advance, the fault is avoided, and the safety accident is avoided.
The escalator motor operation parameter acquisition method has the advantages that in the data acquisition step, data acquisition is carried out in combination with escalator motor operation parameters, the consistency of data acquisition is guaranteed, and diagnosis failure caused by different working conditions is avoided.
More beneficially, in the data determination step, the determination is made based on whether the characteristic index of the signal converges or not, thereby avoiding external vibration interference and avoiding generation of erroneous determination.
The better and more beneficial is that the self-threshold denoising method is utilized to eliminate the interference of random vibration, improve the signal-to-noise ratio of the signal and more powerfully ensure the validity of the signal.
Finally, the characteristic indexes closely related to the bearing state are selected through the correlation matrix of various characteristic indexes and the states of the main drive bearing and the tensioning frame bearing, so that the characteristic indexes are closely related to the bearing state, and the judgment accuracy is improved.
Finally, the characteristic vectors are formed by the characteristic indexes, and then the characteristic vectors are brought into a regression model to obtain the state evaluation of the bearing, so that the fault of the bearing can be judged, early warning and early maintenance are realized, and the fault and danger in actual operation are avoided.
In some embodiments, SIG _ CVG in the data determination step may be made 0.25 as a determination reference. That is, SIG _ CVG less than 0.25 is convergent and SIG _ CVG greater than or equal to 0.25 is non-convergent.
The judgment reference is set to SIG _ CVG (signal distortion) 0.25, so that interference signals can be identified reasonably, and the vibration signals are prevented from being rejected mistakenly.
In some embodiments, the data culling step k may be 3.
And selecting k to be 3, so that the test requirement is met, and abnormal interference signals can be better removed.
In some embodiments, the feature indices to be further extracted include peak Xp, skewness skew, peak index Cf, and kurtosis.
By solving a plurality of characteristic indexes, omission can be avoided, and incomplete evaluation or evaluation failure of the bearing state can be avoided.
In some embodiments for obtaining multiple characteristic indexes, a characteristic matrix of the characteristic indexes and the states of the main drive bearing and the tensioning frame bearing is further established, and the correlation of the characteristic indexes and the states of the main drive bearing and the tensioning frame bearing is evaluated.
Most importantly, irrelevant or poor-relevant characteristic indexes are removed, and misjudgment on the bearing state is avoided.
In some embodiments for obtaining a plurality of characteristic indexes, a characteristic vector formed by the characteristic indexes is further established according to the correlation between the characteristic indexes and the states of the main drive bearing and the tensioning frame bearing.
Namely, by establishing the characteristic vector, a judgment function precisely related to the characteristic index is obtained, and the bearing state is accurately judged.
In order to obtain an accurate function of the probability that the main drive bearing and the tensioner bracket bearing are normal, we can use the maximum likelihood method to find ω and b with the maximum value of the likelihood function L (ω),
and obtaining a model parameter value by using a maximum likelihood method, and avoiding the risk of evaluation errors caused by the value of the model parameter.
For the convenience of calculation, in the state evaluation step, the maximum value of the likelihood function L (ω), max [ L (ω), is found by a random gradient method]=max(∑[yi*logf(xi)+(1-yi)*log(1-f(xi))])。
The maximum value of the likelihood function L (omega) is obtained by a random gradient method, and the obtaining difficulty is reduced.
The above embodiments are not limited to the technical solutions of the embodiments themselves, and the embodiments may be combined with each other into a new embodiment. The above embodiments are only for illustrating the technical solutions of the present invention and are not limited thereto, and any modification or equivalent replacement without departing from the spirit and scope of the present invention should be covered within the technical solutions of the present invention.

Claims (6)

1. A fault monitoring method for an escalator is characterized by comprising the following steps:
a data acquisition step, namely distinguishing working conditions according to the current and the rotating speed of an escalator motor when the escalator runs, and acquiring vibration signals of a main driving bearing and a tensioning frame bearing according to working conditions;
a data judgment step, judging the validity of the signal by utilizing a quotient function, wherein the quotient function is SIG _ CVG ═max (difference of SIG _ Xpp) represents the difference of the maximum peak value and the maximum peak value at different time instants, SIG _ Xpp represents the peak value of the signal in the period of time, when the quotient function approaches to 0, the signal is free of interference, otherwise, the signal is interfered;
a data elimination step, namely eliminating interference components in the signals by using a self-adaptive Threshold value to obtain effective signals, wherein the Threshold value formula is Threshold (mu + k sigma), mu is the mean value of the signals, sigma is the standard deviation of the signals, and k is a selected value;
a data characteristic index calculating step, which calculates the root mean square RMS, the variance Var and the peak value Xpp of the characteristic index by using effective signal data;
and a state evaluation step, namely establishing a characteristic vector X formed by characteristic indexes, substituting the characteristic vector X into a logarithmic linear regression model, and solving the probability that the main drive bearing and the tensioning frame bearing belong to normal, wherein the logarithmic linear regression model has the formula ofω represents the coefficient of the regression model and b represents the intercept of the regression model;
wherein in the state evaluation step, ω and b are found by a maximum value of the likelihood function L (ω) using a maximum likelihood method,
wherein, in the state evaluation step, the maximum value of the likelihood function L (ω), max [ L (ω), is found by a random gradient method]=max(∑[yi*logf(xi)+(1-yi)*log(1-f(xi))])。
2. An escalator fault monitoring method according to claim 1, wherein in the data judging step, SIG _ CVG-0.25 is used as a judgment reference.
3. The escalator fault monitoring method according to claim 1, wherein in the data removing step, k-3 is selected.
4. The escalator fault monitoring method according to claim 1, wherein in the data characteristic index obtaining step, the characteristic indexes to be obtained further include a peak value Xp, a skewness skew, a peak value index Cf, and a kurtosis.
5. The escalator fault monitoring method according to claim 4, wherein in the data characteristic index obtaining step, a characteristic matrix of characteristic indexes and states of the main drive bearing and the tension frame bearing is further established, and correlation of the characteristic indexes and the states of the main drive bearing and the tension frame bearing is evaluated.
6. An escalator fault monitoring method according to claim 5, characterized in that in the state evaluation step, a feature vector formed by feature indexes is established according to the correlation between the feature indexes and the states of the main drive bearing and the tension frame bearing.
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Publication number Priority date Publication date Assignee Title
JP3121488B2 (en) * 1994-03-02 2000-12-25 株式会社日立エンジニアリングサービス Bearing diagnostic device and escalator
CN102831325B (en) * 2012-09-04 2016-03-30 北京航空航天大学 A kind of bearing fault Forecasting Methodology returned based on Gaussian process
CN106006344B (en) * 2016-07-12 2018-11-16 苏州长风航空电子有限公司 Staircase On-line Fault early warning system and method for diagnosing faults
CN107727395B (en) * 2017-07-21 2019-12-03 中国矿业大学 A kind of Method for Bearing Fault Diagnosis based on full variation and uncompensation distance assessment
CN109615126A (en) * 2018-12-03 2019-04-12 北京天地龙跃科技有限公司 A kind of bearing residual life prediction technique
CN109855875B (en) * 2019-01-15 2020-12-22 沈阳化工大学 Rolling bearing operation reliability prediction method

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